Lectures

Week	Topic(s)	Slide(s)	Additional Resources
Week 0I 02.10	Course Information and Introduction Chapter 1 - Introduction	Lec 1: pdf	- http://aima.cs.berkeley.edu/ - Berkeley AI Materials - CS 188 - CS 188 Archives - Fall 2018 - CS 188 - Video: Pieter Abbeel giving the introductory lecture for the Spring 2014 Berkeley CS 188 course - Video: Dan Klein giving the introductory lecture for the Fall 2013 Berkeley CS 188 course - Stanford AI  - Fall 2022 - CS 221
Week 02 09.10	Chapter 2 - Intelligent Agents	Lec 2 : pdf	- https://inst.eecs.berkeley.edu/~cs188/fa23/assets/notes/cs188-fa23-note01.pdf
Week 03 16.10	Chapter 3 - Problem Solving by informed Search	Lec 3: pdf, pptx	- Video: Dan Klein giving the informed search lecture for the Fall 2012 Berkeley CS 188 course - Video: Pieter Abbeel giving the informed search lecture for the Fall 2013 Berkeley CS 188 course - Video: Fall 2018 CS 188
Week 04 23.10	Chapter 3 - Problem Solving by informed Search	Lec 4: pdf, pptx	- Video: Dan Klein giving the informed search lecture for the Fall 2012 Berkeley CS 188 course - Video: Pieter Abbeel giving the informed search lecture for the Fall 2013 Berkeley CS 188 course - Video: Fall 2018 CS 188
Week 05 30.10	Chapter 4 - Search in Complex Environments	Lec 05: pdf, pptx
Week 06 06.11	Chapter 5 - Constraint Satisfaction Problems	Lec 06: pdf, pptx	- Video: Pieter Abbeel giving the first constraint satisfaction problem lecture for the Spring 2013 Berkeley CS 188 course - Video: Pieter Abbeel giving the second constraint satisfaction problem for the Spring 2014 Berkeley CS 188 course
Week 07 13.11	Chapter 6 - Adversarial Search and Games	Lec 7: pdf, pptx	- Video: Pieter Abbeel giving the adversarial search lecture for the Spring 2014 Berkeley CS 188 course - Video: Pieter Abbeel giving the expectimax lecture for the Spring 2014 Berkeley CS 188 course
Week 08	Midterm Week
Week 09	Chapter 16 - Markov Decision Process	Lec 8: pdf, pptx	- Video: Pieter Abbeel giving the MDPs I lecture for the Spring 2014 Berkeley CS 188 course - Video: Pieter Abbeel giving the MDPs II lecture for the Spring 2014 Berkeley CS 188 course - https://inst.eecs.berkeley.edu/~cs188/fa23/assets/lectures/cs188-fa23-lec08.pdf
Week 10	Chapter 23 - Reinforcement Learning	Lec 9: pdf, pptx	- Video: Pieter Abbeel giving the reinforcement learning I lecture for the Spring 2014 Berkeley CS 188 course - Video: Pieter Abbeel giving the reinforcement learning II lecture for the Spring 2014 Berkeley CS 188 course
Week 11	Chapter 19,20,21 - Machine Learning ML I   : Naive Bayes	Lec 10: pdf, pptx	- https://inst.eecs.berkeley.edu/~cs188/fa23/ - https://inst.eecs.berkeley.edu/~cs188/fa18/ - https://stanford-cs221.github.io/autumn2023/ - https://stanford-cs221.github.io/spring2023/ - PythonDataScienceHandbook - ML - DM Slides CS 221 ML 1 : Linear regression and Linear classification - Local Copy (PDF, code)
Week 12	Chapter 19,20,21 - Machine Learning ML II  :  Perceptrons and Logistic Regression	Lec 11: pdf, pptx	- https://inst.eecs.berkeley.edu/~cs188/fa23/ - https://inst.eecs.berkeley.edu/~cs188/fa18/ - https://stanford-cs221.github.io/autumn2023/ - https://stanford-cs221.github.io/spring2023/ - PythonDataScienceHandbook - ML   CS 221 ML 2 : SGD, feature templates, non-linear features, neural networks  - Local Copy (PDF, code)
Week 13	Chapter 19,20,21 - Machine Learning ML III : Optimization and Neural Networks              Neural Networks II and Decision Trees	Lec 12 : - pdf1, pptx1 - pdf2, pptx2	- https://inst.eecs.berkeley.edu/~cs188/fa23/ - https://inst.eecs.berkeley.edu/~cs188/fa18/ - https://stanford-cs221.github.io/autumn2023/ - https://stanford-cs221.github.io/spring2023/ - PythonDataScienceHandbook - ML CS 221 ML 3 : Backpropagation, K-means, generalization, and best practices  - Local Copy (PDF) - K-means demo from CS221 (html)
Week 14	Chapter 22 - Deep Learning Presentations	Lec 13	- Deep Learning Overview (PPTX) - CS 221 DL - Local Copy (pdf) - Dive into Deep Learning (html) - https://huggingface.co/docs/transformers/index - https://jalammar.github.io/illustrated-transformer/ - https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software - https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-frameworks

 

Grades

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